Microsoft Technology Licensing, LLC

Inbal Sagiv

Oron Nir

Fardau Van Neerden

Maayan Yedidia

Itai Norman

Ika Bar-menachem

David Dyckman

Systems and methods are provided for implementing large-scale density-based clustering functionalities. In examples, a system selects, for a dataset (which may be sampled at 100% or less), an upper bound value and a lower bound value of a neighborhood radius parameter of a density-based clustering algorithm. The system identifies, using a modified ternary search algorithm, an optimal neighborhood radius parameter value, based on the upper and lower bound values, outputs the optimal neighborhood radius parameter value and/or a corresponding optimal number of clusters within the dataset. The modified ternary search algorithm leverages the near-unimodality of the neighborhood radius parameter, while selection of the upper bound value leverages a characteristic in which the neighborhood radius parameter value increases as the sampling rate decreases, and selection of the lower bound value uses ternary search that takes the number of clusters as a parameter instead of the neighborhood radius parameter.

Large-scale density-based clustering

A video indexing system generates descriptive metadata for a video including identifiers for each of multiple detections that each correspond to a select one of multiple subjects that appear in the video. These detections are used to create relational graph data for the video, where the relational graph data includes nodes corresponding to each of the multiple subjects that appear in the video. A knowledge graph is queried with unique identifiers corresponding to the multiple subjects of the video to retrieve implicit relational data for each of the multiple subjects, and a merged relational graph is created by merging the implicit relational data retrieved from the knowledge graph with the relational graph data created for the video. A search engine uses the merged relational graph to identify video content relevant to a user query that is based on an implicit relation. Search results identifying the relevant content are presented on a user device.

Graph-based video indexing to support querying with implicit relations

An ontology matching system performs operations to refine a natural language processing (NLP) model that encodes terms of a first hierarchical ontology and of a second hierarchical ontology as embeddings in a latent space. The operations include performing at least a first round of triplet loss training to decrease separation between select pairs of the embeddings sampled from the different ontologies that satisfy a first hierarchical relation while increasing separation between other pairs of the embeddings that do not satisfy the first hierarchical relation. The system then determines, from the refined NLP model, a stable matching scheme that matches each term in the first hierarchical ontology with a corresponding term of the second hierarchical ontology. Responsive to receiving terms of the first hierarchical ontology from an application, the system uses the stable matching scheme to map each of the terms to corresponding terms of the second hierarchical ontology.

Hierarchical ontology matching with self-supervision

A method for automatically classifying terms of a first ontology into categories of a classification scheme defined with respect to a second ontology includes generating, for each term in the first ontology and each term in the second ontology, an embedding encoding the term and a description of the term. The method further includes adding the generated embeddings to a transformer model and computing, for each pair of the embeddings consisting of a first term from the first ontology and a second term from the second ontology, a similarity metric quantifying a similarity of the first term and the second term. The method still further provides for determining a matching scheme based on the similarity metric computed with respect to each pair of the embeddings, where the matching scheme associates term of the first ontology with one or more relevant categories of the classification scheme defined with respect to the second ontology. The method further provides for returning the one or more relevant categories of the classification scheme that are matched, by the determined matching scheme, to a term of the second ontology received as an input.

Ontology customization for indexing digital content

Examples of the present disclosure describe systems and methods for multichannel audio speech classification. In examples, an audio signal comprising multiple audio channels is received at a processing device. Each of the audio channels in the audio signal is transcoded to a predefined audio format. For each of the transcoded audio channels, an average power value is calculated for one or more data windows in the audio signal. A correlation value is calculated between the average power value for each audio channel and the combined average power value of the other audio channels in the audio signal. Each of the correlation values (or an aggregated correlation value for the audio channels) is then compared against a threshold value to determine whether the audio signal is to be classified as a speech-based communication. Based on the classification, an action associated with the audio signal may be performed.

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Kfar Saba, Israel

Inbal Sagiv